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DTSTART;TZID=America/Los_Angeles:20251110T173000
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SUMMARY:Accelerating Startups to Put Enterprise GenAI Multi-Agent Systems in Production
DESCRIPTION:EVENT LOGISTICS\nThe event is hybrid. The audience can attend in Mountain View\, CA or join on Zoom or YouTube \nValley Research Park (VRP)\n319 North Bernardo Ave\nMountain View\, CA 94043\nFrom the locked front door\, go to the right side of the building. The 2nd door will be open. \nAgenda  \n(one hour earlier this time\, for audience in other time zones)\n5:30 people start to arrive\, enjoy pizza and networking. Test your connection and chat.\n6:00 SFBayACM introduces upcoming events and the speaker\n6:00 speaker presentation starts\n7:15 – 7:30 finish\, depending on Q&A \nTALK DESCRIPTION\n– PROBLEM –\nKey Challenges We Will Address for Different Audience Segments \n1. Startups with Funding\, or Looking for Execution Partners\nYou have a strong vertical application and early investment but now you need to grow quickly while focusing on your core value proposition. You may already have a prototype built from initial experimentation but face the challenge of evolving it into a secure\, multi-tenant\, enterprise grade GenAI platform that is compliant\, transparent\, explainable\, and aligned with human values. \n2. Mid Cap and Large Enterprises\nYour teams have built GenAI prototypes yet struggle to solve real problems or get applications into production consistently. You want to identify and overcome the technical\, operational\, and organizational barriers that keep innovation from scaling. \n3. Investors\, VC’s and Venture Partners\nYou are assessing how to reduce costs and risks in your portfolio companies’ GenAI strategies. Understanding why 95 percent of projects fail\, as reported in MIT’s State of AI in Business 2025\, can help you guide founders toward sustainable and successful models. \n4. Product and Engineering Leaders\nYou have ideas\, and aim to design applications that surprise and delight customers by building intelligence that anticipates user intent\, with a Do What I Mean (DWIM) design principle that separates great products from the rest. \n5. Regulated Industry Executives\nIn sectors such as financial services\, healthcare\, or government\, you must ensure AI systems are reliable\, explainable\, auditable\, and fully compliant with evolving laws and governance standards while maintaining speed and innovation. You may also want to monitor your systems\, to comply with various company or security policies. \nWho Should Attend:\nStartup founders\, investors\, product leaders\, and enterprise innovators who want to understand what it really takes to design\, build and scale GenAI systems that work in the real world. \n– SOLUTION –-\nThis talk will answer problems listed above and more. The speaker will first dive into the MIT report\, to better interpret the context of the survey. Lessons learned on how to help GenAI deployments succeed will be discussed. \nStudies that review knowledge worker productivity gains will be reviewed. \nThe speaker will discuss an architecture for a flexible software accelerator framework that can meet the needs for successful GenAI deployments. The framework can be configured to support different applications to meet specific requirements\, combining best-practice software components and design principles. \nGiven an accelerator software architecture with these layers:\nCcube’s Lumin Lab Accelerator software \n\nIntelliConnect (to connect you to the intelligence of the text and data in your company) This lets users ask questions of your corporate knowledge\, for example.\nGuardiance (To provide text classifications to guard your company\, support compliance and AI alignment)\n\n\nTools and software: \n\nMicroservices architecture\nMCP integrations\nMany file types and data sources (i.e. local\, Google drive\, Gmail\, MongoDB)\nEnterprise applications (i.e. SalesForce\, Service Now)\nGenAI personalization\nworkflow support (n8n\, UiPath)\n\n\nGenAI Tools: \n\nRetrieval Augmented Generation (RAG) with Vector Databases\, such as Weaviate or Pinecone\nUse [Guardrails.ai](http://guardrails.ai) as a foundation for text classification. We built on top conditions\, +/- examples per condition\, Boolean combination of conditions into rules. Support many rule actions and rule testing.\nMulti-Agent with: LangGraph\, LangSmith\nMake explicit the dialogue or work product (medium-term memory) that is passed between many agents and users. This work product is persisted over the employees development of a given task.\nLogging channels: system level (tokens\, LLM calls\, costs\, latency)\, user-agent level chat\, agent & LLM thought traces. Scheming detection rules can monitor the thought traces.\nMeaning Markers (auto tagging text with a hierarchy of tags using Named Entity Recognition (NER)\nDesign of Experiments (DoE) to optimize cost and accuracy by varying: prompts\, tools\, LLMs\, embedding models and other architecture choices as they vary over different client requirements and data.\n\nWith the above accelerator software components\, support a variety of vertical applications\, based on the data loaded and configurations. \n– RESULT –\nThe speaker will show a series of demos relating to financial services and healthcare\, as examples of what can be done with such a framework. \n\nCustomer Support application covering portable computers and cell phones\, using both internal trouble ticked data in SalesForce or ServiceNow\, product manuals in PDF or web form and brand related support web sites.\nA medical related demo\, applying about ⅓ of the 150 or so rules needed to cover all of the HIPAA laws around confidential medical information.\nA finance demo around investment advisors\, insider trading or information leaking\nThe design of an upcoming larger demo\, supporting questions over 3 years of S&P500 company data (annual reports\, proxy reports\, websites).\n\nSPEAKER BIO\nGreg Makowski\, https://www.linkedin.com/in/GregMakowski has been training and deploying AI since 1992\, has been growing Data Science Teams since 2010\, has exited 4 startups and has 6 AI patents granted or in various stages. He is currently the Chief of Data Science at Ccube\, leading GenAI/AI consulting and framework development for enterprise applications. See also: https://www.ccube.com/genai. \nSPONSOR INFORMATION\nFrom vision to execution\, Ccube partners with forward-thinking clients to co-build Apps\, Data\, and GenAI solutions across industries. Ccube has 10+ service lines\, 30+ happy clients\, 90% client retention\, and saved clients ~50% costs on average. \nCcube has Silicon Valley roots\, deep expertise\, customer first approach and leverages lean teams for onsite in US and offshore delivery teams in India. \nWatch for us also on\nhttps://www.ccube.com/\nhttps://www.linkedin.com/company/ccube-inc/\nhttps://aws.amazon.com/marketplace \nAs a way to “thank your sponsor”\, Ccube invites you to share your contact info\, and take a brief survey. A summary of the survey results will be shared at the event. \n#ACM\n#SFbayACM \n#GenAI\n#GenAIApplications\n#EnterpriseApplications\n#EnterpriseAIApplications\n#EnterpriseGenAIApplications\n#RAG\n#RetrievalAugmentedGeneration\n#Guardrails\n#AIsafety\n#GenAIcompliance \n#Ccube\n#LuminAILabs\n#IntelliConnect\n#Guardiance \nRegistration/tickets:\nhttps://www.meetup.com/sf-bay-acm/events/311368675/ \n 
URL:https://svec.org/event/accelerating-startups-to-put-enterprise-genai-multi-agent-systems-in-production/
LOCATION:Valley Research Park\, 319 North Bernardo Ave\, Mountain View\, CA.\, Valley Research Park\, 319 North Bernardo Ave\, Mountain View\, CA\, United States
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